Global surveillance of antimicrobial resistance in food animals using priority drugs maps

Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR can be guided with maps. Here, we mapped AMR prevalence in 7 antimicrobials in Escherichia coli and nontyphoidal Salmonella species across low- and middle-income countries (LIMCs), using 1088 point-prevalence surveys in combination with a geospatial model. Hotspots of AMR were predicted in China, India, Brazil, Chile, and part of central Asia and southeastern Africa. The highest resistance prevalence was for tetracycline (59% for E. coli and 54% for nontyphoidal Salmonella, average across LMICs) and lowest for cefotaxime (33% and 19%). We also identified the antimicrobial with the highest probability of resistance exceeding critical levels (50%) in the future (1.7–12.4 years) for each 10 × 10 km pixel on the map. In Africa and South America, 78% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 77% locations were associated with penicillins or sulphonamides. Our maps highlight diverging geographic trends of AMR prevalence across antimicrobial classes, and can be used to target AMR surveillance in AMR hotspots for priority antimicrobial classes.

All three rounds of literature review were conducted with the following procedure (Supplementary Table 1).First, we screened in total 44,325 titles and abstracts, and excluded 40,702 non-PPS publications.We read 3,623 manuscripts in full, and excluded strain surveys, surveys on diseased animals, surveys conducted on a mixture of animal species, surveys without subnational geographic information, and other non-PPS surveys.After the exclusion, there were 1,360 PPS suitable for AMR mapping purposes.We further excluded animal species with small sample sizes such as camel and buffalo, and excluded drug-pathogen combinations not considered in this analysis such as Campylobacter and erythromycin.After the exclusion, 1,088 PPS that reported resistance prevalence in Escherichia coli and nontyphoidal Salmonella spp to 7 antimicrobials (listed in Methods section) were retained for the analyses.All data used in the current analyses are available in the supplementary file, and can also be downloaded at https://resistancebank.org.
Antimicrobial susceptibility testing in the PPS was conducted using either diffusion methods or dilution methods.The majority of PPS used diffusion methods, including disk diffusion (79%) and E-test (0.2%).The rest of PPS used dilution methods, including broth dilution (14%), agar dilution (5%), and automated devices such as VITEK2 (2%).Among the PPS, there was no systematic difference in the measurements between these two families of methods 1 .In each PPS, antimicrobial susceptibility testing results are compared with breakpoints to determine resistance, which are provided by laboratory guidelines and revised annually.Only 18% of records reported the breakpoints used.However, the majority (93%) of PPS mentioned the name of laboratory guidelines used, and 66% among these also mentioned the year of the guideline.The guidelines mentioned by the PPS included guidelines published by the Clinical & Laboratory Standards Institute (96%), the European Committee on Antimicrobial Susceptibility Testing (3%), and the French Society of Microbiology (1%).We adjusted for variations of breakpoints used between surveys, using a method developed by Van Boeckel and Pires et al. 2019 in section "Harmonization of Antimicrobial Resistance Rates" in the Supplementary Material of the reference publication 1 .The adjustment resulted in 635 (2%) resistance prevalence being revised.

Imputation of missing data on resistance prevalence for mapping priority antimicrobials
Missing resistance prevalence data in the point-prevalence surveys were imputed using Multivariate Imputation by Chained Equations (MICE) 2 .Using MICE, a set of plausible values for the missing resistance prevalence could be inferred from the distribution of reported resistance prevalence data, using specified imputation models.The prediction accuracy of three imputation models were compared: Bayesian linear regression (BLR), LASSO regression (LASSO-GLM), and feed-forward neural network (NN).For NN, we selected the optimal combination of hyperparameters, by comparing the root-mean-square error (RMSE) of the imputed values created using NN models with 500 different hyperparameters.These hyperparameters were drawn randomly from the following ranges: the number of nodes of the hidden layer between 1 to 272, dropout rate between 0.2 and 0.8, and learning rate between 0.00001 and 0.1.The lowest value of RMSE was generated with 145 nodes on the hidden layer, a dropout rate of 0.4, and a learning rate of 0.0001.
The comparison of imputation methods was conducted as following.First, we selected a subset of 272 surveys, which contained no missing values of resistance prevalence for the 7 antimicrobials listed in the Methods section.Second, we conducted 50 Monto Carlo simulations to estimate the accuracy of each imputation method.Concretely, for each simulation, we randomly removed 2 out of 7 reported antimicrobial resistance prevalence in each survey, and conducted 4-fold spatial cross validation to impute these deleted values back.These 4 spatial folds were determined based on the continents of the survey locations: America, Africa, western Asia, and eastern Asia.Finally, we compared the RMSE of the imputed missing values of each fold for all Monto Carlo simulations, by running MICE with different imputation methods.The prediction accuracy of LASSO-GLM (RMSE 26.6) outperformed BLR (RMSE 28.7) and NN (RMSE 27.0).Additionally, adding an ad-hoc step of predictive mean matching, and including additional covariates in the imputation process did not improve the prediction accuracy.

Mapping resistance prevalence for each antimicrobial
We mapped the prevalence of resistance for each antimicrobial using Gaussian process stacked generalization 3 .The mapping procedure included two steps.In the first step, we trained three 'child models' to predict resistance prevalence based a set of environmental and anthropogenic covariates (Supplementary Table 3).For each antimicrobial, we also included its estimated amount of use divided by the estimated biomass of food animals in 2020 4 as a covariate in the corresponding child models.The child models included boosted regression trees 5 (BRT), least absolute shrinkage and selection operator applied to linear regression 6 (LASSO-GLM), and feed-forward neural network implemented in Keras 7 (FFNN).The models were trained using four-fold spatial-cross validation (Supplementary Figure 15).For the BRT model, we applied a tree complexity of 3 with 50 initial trees, a learning rate of 0.0005, and a step size of 50.For the NN model, we applied one hidden layer with 31 nodes, a dropout rate of 0.49 and a learning rate of 0.01, using adaptive moment estimation optimizer, and the rectified linear activation function for each layer.
In the second step, the child model predictions were stacked using Gaussian process regression, fitted using the integrated nested Laplace approximations (INLA) 8 .This second step allowed to simultaneously capture the influence of environmental and anthropogenic covariates, as well as the residual spatial correlation.INLA is a deterministic method for Bayesian inference in latent Gaussian modelling, and is comparatively faster than other inference methods such as Markov chain Monte Carlo.The INLA formula included the child model predictions of resistance prevalence as fixed effects, and the spatial autocorrelation as a random effect.The coefficients of the fixed effects were constrained between 0 and 1, such that the coefficients approximately sum to one 3 .The residual spatial correlation was modelled as a Gaussian Markov random field (GMRF) with a Matern covariance function.The prior of the range for the covariance function was set at 4.06 decimal degrees, or roughly 487 km at equator, based on previous work on spatial correlation of AMR 9 .We constructed the meshon which the GMRF representation was builtusing a cutoff of 0.005 decimal degrees, a maximum edge of 1 and 4 decimal degrees, an offset of 0.25 and 1.5 decimal degrees, for the inner domain and outer extension respectively.

Table 2 .
Coefficients associated with logistic regressions on temporal trends of resistance prevalence for Africa, Asia and America.Significant (p < 0.05) coefficient values are shown in bold.No adjustments are made for multiple comparisons.

Table 4 .
The average estimated time for resistance prevalence to exceed 50% across all pixels on the map, for each antimicrobial class and weighted by the distribution of animals' biomass.

Table 5 .
Coefficients of LASSO regressions predicting the possibility that resistance prevalence of an antimicrobial will exceed 50% in the future, given the preceding resistance profile.The antimicrobials included cefotaxime (CTX), sulfamethoxazoletrimethoprim (SXT), chloramphenicol (CHL), tetracycline (TET), ampicillin (AMP), ciprofloxacin (CIP), and gentamicin (GEN).Proportion_PPS: the proportion of point prevalence surveys reporting an increased resistance prevalence to over 50% for an antimicrobial, out of all alternative antimicrobials; Proportion_AMU: the proportion of usage (kg) of an antimicrobial out of all alternative antimicrobials; N50: the number of antimicrobials with resistance above 50% in the preceding resistance profile.The abbreviations of the other covariates are explained in Supplementary Table3.Covariates for which coefficients were 0 for all antimicrobials were removed from the table.

Table 6 .
The top 20 antimicrobial compounds with reported resistance prevalence and the corresponding antimicrobial classes in point prevalence surveys.

Table 7 .
Estimated parameters of the fitted INLA models predicting the geographic distribution of resistance prevalence of each antimicrobial.The table showed the mean value and standard deviation for the range of the spatial random effect, and the coefficients of three child models.The antimicrobials included cefotaxime (CTX),

Table 8 .
Common resistance profiles in point prevalence surveys, with 2, 3, and 4 antimicrobials with resistance higher than 50% (N50).The total number of surveys for each N50 category, and the number of surveys reporting each resistance profile were shown in the brackets.

Table 9 .
Number of point-prevalence surveys conducted on chicken, pigs, and cattle in each year.

Table 10 .
Number of point-prevalence surveys conducted on chicken, pigs, and cattle in each country.

Table 11 .
The percentage of 10 x 10 km pixels in each country that have an uncertainty of the predicted priority antimicrobial above 40%.